Roadway-Crossing-Anomaly Detection System and Method

ABSTRACT

A method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like.

BACKGROUND

Field of the Invention

This invention relates to vehicular systems and more particularly tosystems and methods for perceiving certain anomalies in a road surfaceand responding appropriately.

Background of the Invention

To provide, enable, or support functionality such as driver assistance,controlling vehicle dynamics, and/or autonomous driving, a vehicle needsto accurately perceive the environment through which it is driving.Unfortunately, some anomalies found in a driving environment may createunique challenges, depending on various factors. Accordingly, what isneeded is a system and method for improving how such anomalies arerecognized and how a vehicle prepares to encounter them.

BRIEF DESCRIPTION OF THE DRAWINGS

In order that the advantages of the invention will be readilyunderstood, a more particular description of the invention brieflydescribed above will be rendered by reference to specific embodimentsillustrated in the appended drawings. Understanding that these drawingsdepict only typical embodiments of the invention and are not thereforeto be considered limiting of its scope, the invention will be describedand explained with additional specificity and detail through use of theaccompanying drawings, in which:

FIG. 1 is a schematic diagram illustrating a vehicle comprising a systemin accordance with the present invention;

FIG. 2 is a schematic diagram illustrating a driving environmentcomprising anomalies;

FIG. 3 is a schematic block diagram of one embodiment of a system inaccordance with the present invention;

FIG. 4 is a schematic block diagram of an embodiment of a first methodthat may be executed by a system in accordance with the presentinvention;

FIG. 5 is a schematic block diagram of an embodiment of a second methodthat may be executed by a system in accordance with the presentinvention;

FIG. 6 is a schematic block diagram of an embodiment of a third methodthat may be executed by a system in accordance with the presentinvention; and

FIG. 7 is a schematic block diagram of an embodiment of a fourth methodthat may be executed by a system in accordance with the presentinvention.

DETAILED DESCRIPTION

It will be readily understood that the components of the presentinvention, as generally described and illustrated in the Figures herein,could be arranged and designed in a wide variety of differentconfigurations. Thus, the following more detailed description of theembodiments of the invention, as represented in the Figures, is notintended to limit the scope of the invention, as claimed, but is merelyrepresentative of certain examples of presently contemplated embodimentsin accordance with the invention. The presently described embodimentswill be best understood by reference to the drawings, wherein like partsare designated by like numerals throughout.

Referring to FIG. 1, a system 10 in accordance with the presentinvention may enable a vehicle 12 to better prepare for certain types ofanomalies in a driving environment. A system 10 may do this in anysuitable method. For example, a system 10 may be embodied as hardware,software, or some combination thereof.

In certain embodiments, a system 10 may include a computer 14 and one ormore sensors 16. The computer 14 and sensors 16 may be carried on-boarda vehicle 12. Accordingly, those components 14, 16 may each becharacterized as “on-board” components. In operation, one or moresensors 16 may output signals and a computer 14 may use datacorresponding to or derived from those signals to perceive certainroad-surface anomalies and prepare the vehicle 12 to encounter thoseanomalies.

In selected embodiments, certain on-board sensors 16 may be forwardlooking. Forward looking sensors 16 may monitor a road surface ahead ofa vehicle 12. Such forward looking sensors 18 a may be used for driverassistance, controlling vehicle dynamics, and/or autonomous driving. Forexample, data derived from or corresponding to such sensors 16 may beused to identify certain anomalies and their position relative to acorresponding vehicle 12. Once such anomalies are identified, a system10 may enable a vehicle 12 to prepare to successfully navigate them.

Referring to FIG. 2, in selected embodiments, a real-world drivingenvironment 18 may comprise a driving surface 20 (e.g., road 20) andvarious anomalies 22 distributed across the driving surface 20. Theanomalies 22 in a driving environment 18 may be features or objects thatintermittently or irregularly affect the operation of vehicles 12 in thereal world. Certain such anomalies 22 such a railroad tracks 22 a andcattle guards 22 b may create unique challenges, depending on variousfactors such as vehicular speed, curvature of the road 20, temperature,precipitation, or the like.

Railroad tracks 22 a are found in various geographical regions andintersect roads 20 at many locations. Railroad tracks 22 a are made ofmetal, while the roads 20 they cross are typically not made of metal.This difference in material introduces sudden change in frictionalcoefficients when a vehicle 12 drives from a road surface over arailroad track 22 a and back to the road surface. Under rain or snowconditions, such a crossing may cause a vehicle 12 to become unstableand deviate from its desired trajectory. These undesirable effects maybe magnified when a railroad track 22 a crosses a road 20 at an angle 24other than ninety degrees, crosses a road 20 at a bend or curve in thatroad 20, or a combination thereof.

Cattle guards 22 b may cause similar problems. In grazing or rangeareas, a cattle guard 22 b may be used in the place of a gate. A vehicle12 may drive over a cattle guard 22 b, but cattle will not typicallywalk over one. Accordingly, the cattle guard 22 b may function as a gate(i.e., block the passage of cattle), without requiring any opening orclosing in order to permit a vehicle 12 to pass.

Cattle guards 22 b may be actual or virtual. An actual cattle guard 22 bmay include multiple rails that extend parallel to one another and,typically, orthogonal to the surrounding road 20. The rails may bespaced apart a distance that is significant with respect to (e.g., aboutequal in width to) the hooves of cattle, but insignificant (e.g., atleast passable) to the wheels of a vehicle 12. The rails may be narrowenough that cattle cannot confidently step thereon to cross the cattleguard 22 b. Moreover, below the spaced rails may be open space.Accordingly, cattle may recognize the uncertain footing provided by acattle guard 22 b and not make an attempt to cross it.

The rails of an actual cattle guard 22 b are often made of metal, amaterial dissimilar from the surface of the surrounding road 20. Thisdifference in material may introduce abrupt changes in frictionalcoefficients as a vehicle 12 drives over a cattle guard 22 b and back toa road surface. Under rain or snow conditions these abrupt changes maycause a vehicle 12 to become unstable and deviate from its desiredtrajectory. These undesirable effects may be magnified by certainoscillations the rails of a cattle guard 22 b induce in the suspensionof the vehicle 12.

A virtual cattle guard 22 b may remind cattle of one or more actualcattle guards 22 b they have previously encountered and, therefore,produce the same barrier or blocking effect. However, a virtual cattleguard 22 b may not provide the same uncertain footing that is providedby an actual cattle guard 22 b. For example, a virtual cattle guard 22 bmay include multiple lines that are painted or adhered onto a road 20 toextend parallel to one another and, typically, orthogonal to thesurrounding road 20. The lines may be spaced apart a distance thatresembles that of an actual cattle guard 22 b. Accordingly, cattleconditioned to actual cattle guards 22 b may not see sufficientdifference in a virtual cattle guard 22 b to ever test it.

A virtual cattle guard 22 b may have certain advantages over an actualcattle guard 22 b. A virtual cattle guard 22 b may be much lessexpensive to install. Additionally, a virtual cattle guard 22 b may bemuch less disruptive to the vehicles 12 passing thereover. Specifically,a virtual cattle guard 22 b may not cause the traction issues andsuspension oscillations corresponding to an actual cattle guard 22 b.

In selected embodiments, a system 10 may lower the adverse effectsassociated with crossing railroad tracks 22 a, cattle guards 22 b, andthe like. For example, forward looking sensors 16 may monitor a roadsurface ahead of a vehicle 12. Accordingly, the data from such sensors16 may be used to perceive railroad tracks 22 a and cattle guards 22 b,distinguish between virtual cattle guards 22 b and actual cattle guards22 b, implement appropriate responses or preparations when suchanomalies 22 are perceived, or the like or combinations orsub-combinations thereof.

Referring to FIG. 3, in selected embodiments, a system 10 in accordancewith the present invention may be self contained and operate independentof any other computer system or hardware that is not carried on-boardthe corresponding vehicle 12. Alternatively, a system 10 may communicateas necessary with at least one remote computer via a communicationnetwork (e.g., a cellular network, satellite network, local areawireless network, or the like). This may allow perception or controlalgorithms, machine learning, knowledge of the location and effects ofone or more anomalies 22, or the like that correspond to (e.g., weredeveloped on) one vehicle 12 to be passed to and used by another vehicle12. In still other embodiments, certain components or functionalelements of a system 10 may be located somewhere other than on-board acorresponding vehicle 12. Accordingly, the functionality associated witha system 10 may be performed on-board a vehicle 12, off-board a vehicle12, or some combination thereof.

In selected embodiments, a computer 14 portion of a system 10 inaccordance with the present invention may comprise computer hardware andcomputer software. The computer hardware of a computer 14 may includeone or more processors 26, memory 28, a user interface 30, otherhardware, or the like or a combination or sub-combination thereof. Thememory 28 may be operably connected to the one or more processors 26 andstore the computer software. This may enable the one or more processors26 to execute the computer software.

A user interface 30 of a computer 14 may enable an engineer, technician,or a driver to interact with, customize, or control various aspects of acomputer 14. In selected embodiments, a user interface 30 of a computer14 may include one or more buttons, keys, touch screens, pointingdevices, or the like or a combination or sub-combination thereof. Inother embodiments, a user interface 30 of a computer system 14 maysimply comprise one or more connection ports, pairings, or the like thatenable an external computer to interact or communicate with the computer14.

In selected embodiments, the memory 28 of a computer 14 may storesoftware programmed to use data corresponding to one or more sensors 16to perceive and prepare for one or more anomalies 22 in a drivingenvironment 18. Such software may have any suitable configuration. Incertain embodiments, the software of a computer 14 may include avehicle-motion module 32, perception module 34, control module 36,machine-learning module 38, or the like or a combination orsub-combination thereof. Alternatively, or in addition thereto, memory28 may store one or more anomaly records 40, other data or software 42,or the like or a combination or sub-combination thereof.

A vehicle-motion module 32 may use data corresponding to one or moresensors 16 to determine how a corresponding vehicle 12 is moving. Inselected embodiments, this may be accomplished by combining sensor data(e.g., data corresponding to or derived from one or more sensors 16)characterizing driver controlled parameters such as velocity, drivetorque, brake actuation, steering input, or the like with dataindicative of a current attitude or orientation of a body of the vehicle12 to obtain motion information articulating the current motion state ofthe body of the vehicle 12.

In selected embodiments, a vehicle-motion module 32 may use sensor datacharacterizing driver controlled parameters to obtain or define a vectorsetting forth a direction of travel and velocity at a particular momentin time. Sensor data indicative of a current attitude or orientation ofa body of the vehicle 12 may corresponding to one or more inertialmeasurement units, gyroscopes, accelerometers, or the like orcombinations or sub-combinations thereof. A vehicle-motion module 32 mayuse such sensor data to define one or more parameters such as pitch,roll, and yaw of a body of the vehicle 12. Accordingly, by using varioustypes of sensor data, a vehicle-motion module 32 may output motioninformation that substantially completely estimates and articulates themotion of the body of vehicle 12 at a given moment in time. In selectedembodiments, a vehicle-motion module 32 may collect such motion orattitude data or a portion thereof from a controller area network (CAN)bus of a vehicle 12.

A perception module 34 may analyze data corresponding to one or moresensors 16 in an effort to perceive one or more anomalies 22 within thepath of the corresponding vehicle 12. For example, a perception module34 may analyze data corresponding to one or more forward-looking sensors16 to perceive one or more anomalies 22 ahead of a vehicle 12.Alternatively, or in addition thereto, a perception module 34 mayanalyze data corresponding to one or more sensors 16 that characterizehow a vehicle 12 experiences (e.g., reacts when driving over) one ormore anomalies 22. In certain embodiments, a perception module 34 maycollect selected data characterizing how a vehicle 12 experiences one ormore anomalies 22 from a controller area network (CAN) bus of a vehicle12.

In selected embodiments, a perception module 34 may determine, quantify,estimate, or the like the type of anomaly 22, distance from the vehicle12 to the anomaly 22, time before the vehicle 12 encounters the anomaly22, angle 24 of the anomaly 22 with respect to the road 20, curvature ofthe road 20 when the anomaly 22 is encountered, or the like orcombinations or sub-combinations thereof.

A control module 36 may control how a vehicle 12 responds to (e.g.,prepares to encounter) one or more anomalies 22 identified or perceivedby a perception module 34. The responses issued by a control module 36may vary from simply warning a human driver of an approaching anomaly 22to controlling a function that has traditionally been left to a humandriver (e.g., controlling speed of a vehicle 12 by letting off on thethrottle or braking, controlling the steering of a vehicle 12, or thelike). Accordingly, depending on the nature of the anomaly 22, the timeuntil the vehicle 12 reaches the anomaly 22, and the configuration ofthe system 10, a control module 36 may provide information to a humandriver (e.g., flash a warning light, vibrate a seat, or sound an alarm),assist a human driver (e.g., change a suspension configuration orterminate cruise control), act in the place of a human driver (e.g.,actively brake or steer a vehicle 12), or the like or a combination orsub-combination thereof.

A machine-learning module 38 may support the operation or expand thefunctionality of a vehicle-motion module 32, perception module 34,control module 36, or the like or combinations or sub-combinationsthereof. For example, a machine-learning module 38 may develop, improve,or implement one or more algorithms corresponding to a perception module34 that differentiate between and/or identify various anomalies 22.Similarly, a machine-learning module 38 may develop, improve, orimplement one or more algorithms corresponding to a control module 36that identify appropriate responses for various anomalies 22. Inselected embodiments, a machine-learning module 38 may be structured asa deep neural network.

An anomaly record 40 may comprise data characterizing a particularanomaly 22 located on a particular road 20. The data may reflect how theparticular anomaly 22 was perceived from one or more forward lookingsensors 16, how driving over the particular anomaly 22 affected thevehicle 12, or the like or a combination thereof. One or more anomalyrecords 40 may be used as training data or the like by amachine-learning module 38. Alternatively, or in addition thereto, oneor more anomaly records 40 may be used to prepare for the particularanomaly 22 should the vehicle 12 (or some other vehicle 12 connected viaa computer network) drive over that particular road 20 again.

In selected embodiments, a system 10 may include various components inaddition to a computer 14. For example, a system 10 may include a dataacquisition system 44, a sensor suite 46 comprising one or more sensors16, an actuator suite 48 comprising one or more actuators 50, otherhardware 52 as desired or necessary, or the like or a combination orsub-combination thereof.

In certain embodiments, a data acquisition system 44 may sample signalsoutput by one or more sensors 16 and convert the resulting samples intoinputs (e.g., digital numeric values) that can be manipulated by acomputer 14. For example, a data acquisition system 44 may convertsignals in the form of analog waveforms into inputs in the form ofdigital values suitable for processing. In certain embodiments, a dataacquisition system 44 may include conditioning circuitry that convertssignals output by one or more sensor 16 into forms that can be convertedto digital values, as well as analog-to-digital converters to performsuch converting.

A sensor suite 46 may comprise the one or more sensors 16 carriedon-board a vehicle 12. Certain such sensors 16 may each comprise atransducer that senses or detects some characteristic of an environmentand provides a corresponding output (e.g., an electrical or opticalsignal) that defines that characteristic. For example, one or moresensors 16 of a sensor suite 46 may be accelerometers that output anelectrical signal characteristic of the proper acceleration beingexperienced thereby. Such accelerometers may be used to determine theorientation, acceleration, velocity, and/or distance traveled by avehicle 12. In certain embodiments, a sensor suite 46 of a system 10 mayinclude one or more cameras 16 a, laser scanners (e.g., LiDAR scanners16 b), radar devices 16 c, global positioning systems 16 d, temperaturesensors 16 e, powertrain sensors 16 f, attitude sensors 16 g, othersensors 16 h (e.g., ultrasonic transducers), or the like or combinationsor sub-combinations thereof.

In selected embodiments, one or more cameras 16 a, LiDAR scanners 16 b,radar devices 16 c, or the like or combination or sub-combinationsthereof may be forward looking sensors 16 useful in characterizing orperceiving one or more anomalies 22 located ahead in the path of thevehicle 12. A global positioning system 16 d may provide location and/orspeed data to a computer 14. In selected embodiments, location dataderived from a global positioning system 16 d may incorporated into ananomaly record 40. Accordingly, as a current location of a vehicle 12approaches a known location of an anomaly 22, a control module 36 mayprepare a vehicle 12 to encounter than anomaly 22.

A thermometer 16 e may characterize an ambient temperature surrounding avehicle 12. Accordingly, ambient temperature may be factored into howbest to respond to a particular anomaly 22. For example, coldertemperatures may be associated with ice, snow, and/or other causes oflower traction or frictional engagement between the tires of a vehicle12 and a driving surface 20. Accordingly, if the ambient temperature isnear, at, or below freezing, a control module 36 may implement a moreconservative approach or response to an anomaly 22 than if the ambienttemperature is well above freezing.

Powertrain sensors 16 f may include one or more devices characterizingthe speed of a vehicle, engine torque, wheel slippage, or the like.Accordingly, the speed of a vehicle, engine torque, wheel slippage, orthe like may be factored into how best to respond to a particularanomaly 22. For example, if, during a particular drive, wheel slippageoccurs while the engine torque is relatively low, it may be an indicatorof poor traction or frictional engagement between the tires of a vehicle12 and a driving surface. Accordingly, during that drive, a controlmodule 36 may implement a more conservative approach or response to oneor more anomalies 22. In selected embodiments, powertrain sensors 16 fmay include one or more revolution counters or sensors, strain gauges,or the like or combinations thereof.

Attitude sensors 16 g may include one or more devices characterizing theorientation of a vehicle 12 or some component thereof with respect to auniversal coordinate system, vehicular coordinate system, road surface,or the like or combinations thereof. Accordingly, orientation of avehicle 12 may be factored into how best to respond to a particularanomaly 22. For example, if one or more attitude sensors 16 f indicatethat vehicle 12 is turning (e.g., experiencing a lateral acceleration,has front wheels pointing somewhere other than straight ahead), it maybe an indicator that certain lateral forces or considerations may needto be taken into account when deciding how best to prepare for ananomaly 22. In selected embodiments, attitude sensors 16 g may includeone or more gyroscopes, inertial measurement units, rotation sensors,strain gauges, or the like or a combination or sub-combination thereof.

An actuator suite 48 may comprise the one or more actuators 50 carriedon-board a vehicle 12. Such actuators 50 may operate under the directionof a control module 36. Accordingly, the actuators 50 may implement oreffect the responses to anomalies 22 dictated by the control module 36.For example, one or more actuators 50 of an actuator suite 48 may, underthe direction of a control module 36, provide information to a humandriver (e.g., flash a warning light, vibrate a seat, or sound an alarm),assist a human driver (e.g., change a suspension configuration orterminate cruise control), act in the place of a human driver (e.g.,actively brake or steer a vehicle 12), or the like or a combination orsub-combination thereof.

Certain actuators 50 within an actuator suite 48 may each comprise aswitch or controller that, when activated or instructed, turns somethingon or off. Certain other such actuators 50 within an actuator suite 48may each comprise a transducer that receives an input signal (e.g., anelectrical signal or current) and produces a particular motion inresponse thereto. For example, one or more actuators 50 of an actuatorsuite 48 may be or comprise solenoids that convert electrical energyinto motion (e.g., linear motion).

In certain embodiments, an actuator suite 48 of a system 10 may includeone or more speed actuators 50 a, steering actuators 50 b, suspensionactuators 50 c, other actuators 50 d, or the like or a combination orsub-combination thereof. A speed actuator 50 a may control the speed ofa vehicle 12 by letting off the throttle, turning off cruise control,applying the brakes, or the like. A steering actuator 50 b may steer avehicle 12. A suspension actuator 50 c may control or adjust some aspectof a suspension system of a vehicle 12.

For example, in selected embodiments, a suspension actuator 50 c mayadjust or control in some manner the vertical movement of one or morewheels relative to the chassis or body of a vehicle 12. This may includefirming or softening a suspension, increasing or decreasing a rideheight, increasing or decreasing the frequency of oscillation of asuspension system, or the like or combinations or sub-combinationsthereof.

In certain embodiments, one or more other actuators 50 d included withina actuator suite 48 may include or comprise communication actuators.Communication actuators may not affect the handling, driving, orperformance of a vehicle 12. Rather, they may provide, initiate, orterminate certain communications or messages directed to a driver of avehicle 12. For example, one or more communication actuators may, underthe direction of a control module 36, start or stop a warning light ortext-based message, vibration of a seat or steering wheel, an audiblealarm or spoken message, or the like or a combination or sub-combinationthereof.

Referring to FIG. 4, a system 10 may support, enable, or execute aprocess 54 in accordance with the present invention. In selectedembodiments, such a process 54 may begin with analyzing 56 data orsignals corresponding to one or more sensors 16. Based on that analyzing56, an anomaly 22 such as a railroad track 22 a, cattle guard 22 b, orthe like may be perceived 58. Perceiving 58 the anomaly 22 may includeidentifying the anomaly 22 (e.g., differentiating between railroad track22 a, actual cattle guard 22 b, virtual cattle guard 22 b, or the like),estimating a distance from the vehicle 12 to the anomaly 22, determiningan orientation or angle 24 of the anomaly 22 with respect to the road20, determining curvature of the road 20 in the area surrounding theanomaly 22, or the like or a combination or sub-combination thereof.

One or more inputs (e.g., data from one or more sensors 16) may be used60 to determine current motion of a vehicle 12. This information may beused to determine how best to prepare to encounter an anomaly 22. Forexample, if the speed of a vehicle 12 and the distance to the anomaly 22are known or determined, then a system 10 may calculate when the vehicle12 will encounter the anomaly 22. In selected embodiments, one or moreinputs may be used 60 to determine speed of a vehicle, orientation ofthe vehicle 12 with respect to a road 20, orientation of the vehicle 12with respect to the anomaly 22, current steering direction, currentsuspension settings or configuration, or the like or a combination orsub-combination thereof.

One or more inputs (e.g., data from one or more sensors 16) may be used62 to determine current weather conditions in an area surrounding avehicle 12. This information may be also used to determine how best toprepare to encounter an anomaly 22. For example, if one or more inputsindicate that it is at or below freezing, there is water, ice, or snowon the road 20, or some combination thereof, then the appropriateresponses to an anomaly 22 may take those factors into consideration. Inselected embodiments, one or more inputs may be used 62 to determineambient temperature, presence or absence of precipitation, presence orabsence of water, ice, or snow on a road 20, or the like or acombination or sub-combination thereof.

Once sufficient data regarding an anomaly 22, the current motion of thevehicle 12, and/or the ambient weather conditions has been gathered, asystem 10 may apply 64 a control algorithm to that data to identify anappropriate response (e.g., an appropriate collection of one or moreactions) to the anomaly 22. Depending on various factors, theappropriate response may include: initiating a warning light ortext-based message, vibration of a seat or steering wheel, an audiblealarm or spoken message, changing a suspension configuration;terminating cruise control; letting off the throttle; actively braking;steering a vehicle 12 to encounter an anomaly 22 like a railroad track22 a at an angle 24 as close to ninety degrees as possible given thedimensions of the lane and/or road; or the like or a combination orsub-combination thereof. A system 10 may then implement 66 theappropriate response.

For example, in selected embodiments, anomalies such as railroad tracks22 a, actual cattle guards 22 b, or the like may be detected byacquiring signal from several sensors and processing them with analgorithm. The sensors 16 may include one or more cameras, radardevices, ultrasound devices, LiDAR scanner (e.g., solid state LiDAR),and devices posting data on a controller area network (CAN) bus of avehicle 12. The outputs of one or more such sensors 16 may be taken asan input to a fusion algorithm. The output of this algorithm may includea three dimensional location (e.g., X, Y, and Z) of a railroad track 22a or actual cattle guard 22 b with respect to a body coordinate systemof the vehicle 12.

The algorithm may take input from one or more such sensors to estimatethe location of the railroad tracks 22 a or actual cattle guard 22 bwith reference to the body coordinate system. Additionally, by usinginformation (e.g., pitch, vehicle speed, and height) from the CAN bus,an algorithm may determine a position of the front wheels of the vehicle12 with respect to the railroad tracks 22 a or actual cattle guard 22 b.The outputs of this algorithm, like X, Y and Z location of railroadtracks 22 a or actual cattle guard 22 b, may help the vehicle 12 to bebetter prepared to pass over the railroad tracks 22 a or actual cattleguard 22 b without losing control of the vehicle 12. Additionally, itmay be used to control an active suspension of the vehicle 12 to makethe drive over the railroad tracks 22 a or actual cattle guard 22 b morecomfortable. An output of the algorithm may also warn the driver toprevent the vehicle 12 from being damaged as it drives over the railroadtracks 22 a or actual cattle guard 22 b.

Moreover, with knowledge of existing weather conditions (e.g., snow,ice, sleet, heavy rain, or the like) and an accurate perception of arailroad track 22 a and the trajectory of an autonomous vehicle, asystem 10 in accordance with the present invention may enable thevehicle 12 to anticipate tire slippage and modify its trajectory toprevent a loss of control of the vehicle 12.

For example, a perception module 34 (e.g., a perception module 34employing sensor fusion) may accurately describe a line representing therailroad tracks 22 a as they cross a road 20. When the road 20 has anatural curvature of its own, a vehicle 12 with a heading angle matchingthe road curvature may slip off the road 20, crash into the shoulder ofthe road 20, or otherwise experience a recoverable or non-recoverableloss of control.

To prevent this, a system 10 may use the line representing the railroadtrack and the heading of the vehicle 12 to calculate the angle 24between the two. The system 12 may then institute a steering responsethat pushes the angle 24 between the two toward ninety degrees, or asclose to ninety degrees as possible given lane constraints or the like.Striving for a perpendicular angle of incidence with respect to therailroad tracks may minimize slippage of the wheels of the vehicle 12and help maintain control.

In certain embodiments, a system 10 may attempt not only to set theheading of the vehicle 12 (i.e., both steering tires and followingtires) orthogonal to the railroad tracks 22 a, but also to get theinertial heading of the vehicle 12 completely longitudinal (e.g., nolateral, suspension damped and no yaw) prior to the area of potentialslippage. Since railroad tracks 22 a may freeze prior to other roadsurfaces, a system 10 may seek to make all corrections prior to drivingover the railroad tracks 22 a.

Referring to FIG. 5, a system 10 may support, enable, or execute anotherprocess 68 in accordance with the present invention. In selectedembodiments, such a process 68 may begin with analyzing 56 data orsignals corresponding to one or more sensors 16. Based on that analyzing56, an anomaly 22 may be perceived 58. In certain embodiments, thisperceiving 58 may be based on primary sensor data (i.e., datacorresponding to a primary sensor 16 such as a camera 16 a). One or moreinputs (e.g., data from one or more sensors 16) may be used 60 todetermine current motion of a vehicle 12. One or more inputs (e.g., datafrom one or more sensors 16) may be used 62 to determine current weatherconditions in an area surrounding a vehicle 12. This information may bealso used to determine how best to prepare to encounter an anomaly 22.

However, in certain embodiments or situations, the data from a primarysensor 16 may not be conclusive. For example, if a primary sensor 16 isa camera 16 a, it may be difficult for a system 10 (e.g., a perceptionmodule 34) using just camera data to determine whether a cattle guard 22b is a virtual cattle guard 22 b or an actual cattle guard 22 b.Accordingly, in such embodiments or situations, a system 10 (e.g., aperception module 34) may analyze 70 secondary sensor data correspondingto the location of the anomaly 22. For example, a system 10 may analyze70 radar data, laser data (e.g., LiDAR data), or the like correspondingto the location of the anomaly 22.

The data corresponding to one or more secondary sensors 16 may provideinformation or context that cannot be obtained from the datacorresponding to the one or more primary sensors 16. Accordingly, thesecondary sensor data may enable a system 10 (e.g., a perception module34) to determine 72 whether the particular anomaly 22 in question is ofone type or another (e.g., whether the cattle guard 22 b is a virtualcattle guard 22 b or an actual cattle guard 22 b).

Once sufficient data regarding an anomaly 22, the current motion of thevehicle 12, and/or the ambient weather conditions has been gathered, asystem 10 may apply 74, 76 a control algorithm to that data to identifyan appropriate response to the anomaly 22. In selected embodiments, acontrol algorithm may take into account the type of anomalies 22.Accordingly, a control algorithm may recommend one response (e.g., acollection of one or more actions) for a first type of anomaly 22 andanother response (e.g., a different collection of one or more actions)for a second type of anomaly 22.

For example, a control algorithm may recommend that no action be takenwhen it is determined that the cattle guard 22 b that was perceived 58is a virtual cattle guard 22 b and that certain suspension changes betimely made when it is determined that the cattle guard 22 b that wasperceived 58 is an actual cattle guard 22 b. A system 10 may thenimplement 66 the appropriate response.

For example, an autonomous vehicle 12 may possess one or more radar,camera, and/or LiDAR devices to accurately perceive a drivingenvironment 18. However, if the vehicle 12 is primarily using cameraoutput to detect a cattle guard 22 b, and hence determine the trajectoryof the vehicle 12, a false alarm might be triggered by a virtual cattleguard 22 b. To prevent this, forward facing LiDAR and/or radar data maybe utilized.

The metal used in an actual cattle guard 22 b may give or produce aLiDAR point cloud data with reflectivity values very different from thatof a virtual cattle guard 22 b with its painted lines. Similarly, radarrays may experience multiple reflections from the metal of an actualcattle guard 22 b and hence produce returns comprising a large number ofdetections on that portion of the road 20. Accordingly, a system 10 inaccordance with the present invention may use the detections from LiDARand/or radar devices to confirm the existence of an actual cattle guard22 b and prevent maneuvers that the vehicle 12 need not take when thecattle guard 22 b is merely virtual.

Referring to FIG. 6, a system 10 may support, enable, or execute anotherprocess 78 in accordance with the present invention. In selectedembodiments, such a process 78 may begin with experiencing 80 an anomaly22. For example, a vehicle 12 may experience a railroad track 22 a orcattle guard 22 b by driving over it. The system 10 may then record 82one or more effects of the anomaly 22 on the vehicle 12. Those effectsmay be reflected in data corresponding to one or more sensors 16 that iscollected as the vehicle experiences the anomaly 22. The effects mayinclude certain vibrations, accelerations, wheel slippage, or the likeor a combination or sub-combination thereof.

Analyzing the effects of an anomaly 22, a system 10 may determine 84 howthe anomaly 22 may be approached in the future. For example, the system10 may determine 84 that the anomaly 22 produces little vibration and/orwheel slippage and that it can be traversed at greater speed in thefuture. Conversely, the system 10 may determine 84 that the anomaly 22produced significant vibration that that it should be traversed at aneven slower speed and/or with a different suspension setup in thefuture.

In selected embodiments, the new approach (e.g., a better response tothe anomaly 22) determined 84 by the system 10 may be stored and linked86 in some manner to a location in a driving environment 18.Accordingly, when the vehicle 12 draws 88 near that location sometime inthe future, the new approach to the anomaly 22 may be triggered orotherwise implemented 90. In this manner, a system 10 may be everlearning and improving its response to anomalies 22.

Referring to FIG. 7, a system 10 may support, enable, or execute anotherprocess 92 in accordance with the present invention. In selectedembodiments, such a process 92 may begin with analyzing 56 data orsignals corresponding to one or more sensors 16. Based on that analyzing56, an anomaly 22 such as a railroad track 22 a, cattle guard 22 b, orthe like may be perceived 58. One or more inputs may be used 60 todetermine current motion of a vehicle 12. One or more inputs may be used62 to determine current weather conditions in an area surrounding avehicle 12. This information or a subset thereof may be used todetermine how best to prepare to encounter an anomaly 22.

Once sufficient data regarding an anomaly 22, the current motion of thevehicle 12, and/or the ambient weather conditions has been gathered, asystem 10 may apply 64 a control algorithm to that data to identify anappropriate response to the anomaly 22. A system 10 may then implement66 the appropriate response.

The process 92 may continue with experiencing 80 the anomaly 22. Forexample, the vehicle 12 may drive over a railroad track 22 a, cattleguard 22 b, or other anomaly 22. The system 10 may then record 82 one ormore effects of the anomaly 22 on the vehicle 12. By analyzing theeffects of an anomaly 22, a system 10 may determine 84 how the anomaly22 may be better approached in the future. Accordingly, the system 10may update 94 one or more control algorithms to reflect or incorporateone or more aspects of the better approach. In this manner, a system 10may be ever learning and improving its response to anomalies 22 (e.g.,anomalies 22 that have been experienced by a vehicle 12 and anomalies 22that have not yet been experienced by a vehicle 12).

The flowcharts in FIGS. 4-7 illustrate the architecture, functionality,and operation of possible implementations of systems, methods, andcomputer program products according to certain embodiments of thepresent invention. In this regard, each block in the flowcharts mayrepresent a module, segment, or portion of code, which comprises one ormore executable instructions for implementing the specified logicalfunction(s). It will also be noted that each block of the flowchartillustrations, and combinations of blocks in the flowchartillustrations, may be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should also be noted that, in some alternative implementations, thefunctions noted in the blocks may occur out of the order noted in theFigures. In certain embodiments, two blocks shown in succession may, infact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. Alternatively, certain steps or functions may beomitted if not needed.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrative,and not restrictive. The scope of the invention is, therefore, indicatedby the appended claims, rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

What is claimed is:
 1. A method comprising: receiving, by a computersystem, one or more inputs corresponding to one or more sensors;estimating, by the computer system based on the one or more inputs, amotion of a vehicle with respect to a anomaly extending across a roadahead of the vehicle; and changing, by the computer system, a suspensionsetting of the vehicle to more safely or comfortably drive over theanomaly.
 2. The method of claim 1, wherein at least one of the one ormore sensors comprises a forward-looking sensor characterizing a drivingenvironment ahead of the vehicle.
 3. The method of claim 2, wherein atleast one of the one or more sensors comprises a device providing datato the computer system via a CAN bus of the vehicle.
 4. The method ofclaim 3, wherein the anomaly is a railroad track or a cattle guard. 5.The method of claim 4, wherein the forward looking sensor is selectedfrom the group consisting of an ultrasonic transducer, a laser scanner,a LiDAR device, a radar device, and a camera.
 6. The method of claim 5,wherein the data provided by the device characterizes the attitude ofthe vehicle in at least one of pitch, roll, and yaw.
 7. The method ofclaim 6, wherein: the anomaly is a railroad track; and the estimatingcomprises determining an angle of intersection between the railroadtrack and the road.
 8. The method of claim 7, wherein the changingcomprises controlling, by the computer system, steering of the vehicleto move the angle closer to ninety degrees.
 9. The method of claim 6,wherein: a first sensor of the one or more sensors is a camera; and asecond sensor of the one or more sensors is a radar or LiDAR device. 10.The method of claim 9, wherein the estimating comprises: using, by thecomputer system, data corresponding to the first sensor to determinethat the anomaly is a cattle guard; and using, by the computer system,data corresponding to the second sensor to determine that the cattleguard is an actual cattle guard and not a virtual cattle guard.
 11. Amethod comprising: receiving, by a computer system carried on-board avehicle, one or more inputs corresponding to one or more sensors carriedon-board the vehicle; receiving, by the on-board computer system, datafrom a CAN bus of the vehicle, the data characterizing at least one ofthe speed, pitch, roll, and yaw of the vehicle; estimating, by theon-board computer system based on the one or more inputs and the data,motion of the vehicle with respect to a railroad track or cattle guardcrossing a road ahead of the vehicle; and changing, by the computersystem, a suspension setting of the vehicle to more safely orcomfortably drive over the railroad track or cattle guard.
 12. Themethod of claim 11, wherein at least one of the one or more sensorscomprises a forward-looking sensor characterizing the road ahead of thevehicle.
 13. The method of claim 12, wherein the forward looking sensoris selected from the group consisting of an ultrasonic transducer, alaser scanner, a LiDAR device, a radar device, and a camera.
 14. Themethod of claim 13, wherein: the anomaly is a railroad track; and theestimating comprises determining an angle of intersection between therailroad track and the road.
 15. The method of claim 14, wherein thechanging comprises controlling, by the computer system, steering of thevehicle to move the angle closer to ninety degrees.
 16. The method ofclaim 11, wherein: a first sensor of the one or more sensors is acamera; and a second sensor of the one or more sensors is a radar orLiDAR device.
 17. The method of claim 16, wherein the estimatingcomprises: using, by the computer system, data corresponding to thefirst sensor to determine that the anomaly is a cattle guard; and using,by the computer system, data corresponding to the second sensor todetermine that the cattle guard is an actual cattle guard and not avirtual cattle guard.
 18. The method of claim 11, further comprisingrecording, by the computer system, an effect on the vehicle produced bydriving over the railroad track or cattle guard.
 19. The method of claim18, further comprising: determining, by the computer system based on theeffect, a better approach for preparing the vehicle to driver over therailroad track or cattle guard; linking, by the computer system inmemory thereof, the better approach to a location corresponding to therailroad track or cattle guard; approaching the railroad track or cattleguard a second time; and retrieving and implementing, by the computersystem, the better approach at the vehicle approaches and drives overthe railroad track or cattle guard the second time.
 20. A systemcomprising: a vehicle carrying on-board at least one processor, memoryoperably connected to the at least one processor, a CAN bus, and one ormore forward looking sensors; and the vehicle, wherein the memory storesexecutable instructions programmed to receive one or more inputscorresponding to the one or more forward looking sensors, receive datafrom the CAN bus of the vehicle, the data characterizing at least one ofthe speed, pitch, roll, and yaw of the vehicle, estimate, based on theone or more inputs and the data, motion of the vehicle with respect to arailroad track or cattle guard crossing a road ahead of the vehicle, andchange a suspension setting of the vehicle to more safely or comfortablydrive over the railroad track or cattle guard.